SoroushVahidi/scidocs huggingface dataset:
Efficient Algorithms for Convolutional Sparse Representations
(2016)

When applying sparse representation techniques to images, the standard approach is to independently compute the representations for a set of overlapping image patches. This method performs very well in a variety of applications, but results in a representation that is multi-valued and not optimized with respect to the entire image. An alternative representation structure is provided by a convolutional sparse representation, in which a sparse representation of an entire image is computed by replacing the linear combination of a set of dictionary vectors by the sum of a set of convolutions with dictionary filters. The resulting representation is both single-valued and jointly optimized over the entire image. While this form of a sparse representation has been applied to a variety of problems in signal and image processing and computer vision, the computational expense of the corresponding optimization problems has restricted application to relatively small signals and images. This paper presents new, efficient algorithms that substantially improve on the performance of other recent methods, contributing to the development of this type of representation as a practical tool for a wider range of problems.
Blind Trust Online : Experimental Evidence from Baseball Cards
(2002)

Before reaping transaction cost savings from the Internet, online sellers must credibly communicate the quality of their goods or their reliability of delivery. We use baseball cards as an example to address (1) whether consumers fully understand the risk of online trading, (2) the extent to which consumers employ seller reputation and professional grading to alleviate the risk they are exposed to, and (3) the impact of online trading on the retail market and supporting industries. An experiment is carried out to obtain actual baseball cards from both online and retail markets whose quality is then professionally graded and compared to the prices paid by online buyers for goods with similar claims. Our findings indicate that some uninformed online buyers are misled by non-credible claims of quality; they pay higher prices but do not receive better quality and in fact are defrauded more often. Although a combination of professional grading and reputation mechanisms appear to have the potential to solve the problem, they have only had limited success so far. Data on structural changes in the baseball card industry suggest the problems stemming from buyer misconception may have far-reaching consequences, extending beyond the immediate losses of defrauded consumers to influencing producer and supply decisions. Our findings also suggest that empirical studies using online price data should be careful of what assumptions they are making of online users and what behaviors of buyers and sellers are likely to drive the price data. Department of Economics, University of Maryland, 3115 G Tydings, College Park, MD 20742. Contact Email: jin@econ.umd.edu. We thank Seth Sanders, John List, John Shea, Dan Vincent, David Lucking-Reiley, Larry Ausubel, Peter Cramton, V. Joseph Hotz, Jimmy Chan, Vincent Crawford, attendants in Johns Hopkins UMD conference and participants of UCLA,UCSD and FTC seminars for constructive advice and suggestions. We are grateful to 8 friends who acted as our agents in purchasing baseball cards in retail markets, and numerous sports card store owners for their time and insights regarding the sports card industry. Excellent research assistance from Randy Alexander Moore and Krzysztof Fizyta is gratefully acknowledged. Any remaining errors are ours.
Vehicle location finder using Global position system and Global System for Mobile
(2014)

Each year, the number of stolen vehicle is on the rise. Usually, to prevent theft, a physical type countermeasure is used such as padlock, disk break lock and other more which is a preventive action but it is not enough safe. The objective of this study is to create a controllable system that can display the location of a vehicle using Global position system (GPS) to pin point the location and Global System for Mobile (GSM) as a mean for communicating with the vehicle for ease of finding after a theft attempt. The system is made to test the accuracy of the location that is send to the user when the vehicle is in motion and stationary in the city and suburb. The system is made by combining a micro controller with GPS and GSM, then comparing it with other similar device available in the market like Garmin and a reference website to find the radius of error. The study of proposed device begins by studying IEEE journal about alternative product and the vehicle itself. The hardware and program development is done by research and trial and error as the controller do not interact with both module at the same time, after successfully programming both module, it is combined into a single program with addition of interrupt program. The experiment is done in three set of tests so that the system accuracy can be determine when stationary and in motion on vehicle, output controlling is the test to determine if the controller can be made into anti-theft system. The result of the test concludes that the system can provide standard GPS coordinate when requested via Short Message Service (SMS). The system can also be used to control an actuator.
Time is of the essence: improving recency ranking using Twitter data
(2010)

Realtime web search refers to the retrieval of very fresh content which is in high demand. An effective portal web search engine must support a variety of search needs, including realtime web search. However, supporting realtime web search introduces two challenges not encountered in non-realtime web search: quickly crawling relevant content and ranking documents with impoverished link and click information. In this paper, we advocate the use of realtime micro-blogging data for addressing both of these problems. We propose a method to use the micro-blogging data stream to detect fresh URLs. We also use micro-blogging data to compute novel and effective features for ranking fresh URLs. We demonstrate these methods improve effective of the portal web search engine for realtime web search.
Mining asynchronous periodic patterns in time series data
(2000)

Periodicy detection in time series data is a challenging pro blem of great importance in many applications. Most previous work focused on mining synchronous p eriodic patterns and did not recognize misaligned presence of a pattern due to the intervention of r andom noise. In this paper, we propose a more flexible model of asynchronous periodic pattern that ma y be present only within a subsequence and whose occurrences may be shifted due to disturbance. Two parametersmin rep andmax dis are employed to specify the minimum number of repetitions th at is required within each segment of non-disrupted pattern occurrences and the maximum allowed disturbance between any two successive valid segments. Upon satisfying these two requirements, th e longest valid subsequence of a pattern is returned. A two phase algorithm is devised to first generate p o ntial periods by distance-based pruning followed by an iterative procedure to derive and validat e candidate patterns and locate the longest valid subsequence. We also show that this algorithm can not o nly provide linear time complexity with respect to the length of the sequence but also achieve space e ffici ncy.
Incremental Fuzzy Clustering With Multiple Medoids for Large Data
(2014)

As an important technique of data analysis, clustering plays an important role in finding the underlying pattern structure embedded in unlabeled data. Clustering algorithms that need to store all the data into the memory for analysis become infeasible when the dataset is too large to be stored. To handle such large data, incremental clustering approaches are proposed. The key idea behind these approaches is to find representatives (centroids or medoids) to represent each cluster in each data chunk, which is a packet of the data, and final data analysis is carried out based on those identified representatives from all the chunks. In this paper, we propose a new incremental clustering approach called incremental multiple medoids-based fuzzy clustering (IMMFC) to handle complex patterns that are not compact and well separated. We would like to investigate whether IMMFC is a good alternative to capturing the underlying data structure more accurately. IMMFC not only facilitates the selection of multiple medoids for each cluster in a data chunk, but also has the mechanism to make use of relationships among those identified medoids as side information to help the final data clustering process. The detailed problem formulation, updating rules derivation, and the in-depth analysis of the proposed IMMFC are provided. Experimental studies on several large datasets that include real world malware datasets have been conducted. IMMFC outperforms existing incremental fuzzy clustering approaches in terms of clustering accuracy and robustness to the order of data. These results demonstrate the great potential of IMMFC for large-data analysis.
M-learning in Nigerian higher education: an experimental study with Edmodo
(2016)

Social media technologies have recently gained remarkable popularity in the education sector. Recent research indicated that students rely on social media for educational purposes like social networking, chatting, and knowledge sharing. With social media aiding learning experiences, m-learning is anticipated to improve the application of social media. In the paper, we investigated the preference of social media tools and mobile devices for learning, their benefits and effectiveness, and how they can possibly improve learning process in Nigeria. Furthermore, we evaluated learning experiences of students using Edmodo social media-based learning environment in a Nigerian university. We used a mixed method research approach, where the data was triangulated from two questionnaires and students’ interviews. Data gathered after the course shows that students learning response was improved. Students were eager to use mobile devices for accessing social media sites, thus providing experimental evidence of the place of social media in m-learning.
Scientific Workflow: A Survey and Research Directions
(2007)

Workflow technologies are emerging as the dominant approach to coordinate groups of distributed services. However with a space filled with competing specifications, standards and frameworks from multiple domains, choosing the right tool for the job is not always a straightforward task. Researchers are often unaware of the range of technology that already exists and focus on implementing yet another proprietary workflow system. As an antidote to this common problem, this paper presents a concise survey of existing workflow technology from the business and scientific domain and makes a number of key suggestions towards the future development of scientific workflow systems.
Deliberation Networks: Sequence Generation Beyond One-Pass Decoding
(2017)

The encoder-decoder framework has achieved promising progress for many sequence generation tasks, including machine translation, text summarization, dialog system, image captioning, etc. Such a framework adopts an one-pass forward process while decoding and generating a sequence, but lacks the deliberation process: A generated sequence is directly used as final output without further polishing. However, deliberation is a common behavior in human’s daily life like reading news and writing papers/articles/books. In this work, we introduce the deliberation process into the encoder-decoder framework and propose deliberation networks for sequence generation. A deliberation network has two levels of decoders, where the first-pass decoder generates a raw sequence and the second-pass decoder polishes and refines the raw sentence with deliberation. Since the second-pass deliberation decoder has global information about what the sequence to be generated might be, it has the potential to generate a better sequence by looking into future words in the raw sentence. Experiments on neural machine translation and text summarization demonstrate the effectiveness of the proposed deliberation networks. On the WMT 2014 English-to-French translation task, our model establishes a new state-of-the-art BLEU score of 41.5.
Real-Time Semantic Segmentation with Label Propagation
(2016)

Despite of the success of convolutional neural networks for semantic image segmentation, CNNs cannot be used for many applications due to limited computational resources. Even efficient approaches based on random forests are not efficient enough for real-time performance in some cases. In this work, we propose an approach based on superpixels and label propagation that reduces the runtime of a random forest approach by factor 192 while increasing the segmentation accuracy.
Mitigation of Denial of Service Attack with Hardware Trojans in NoC Architectures
(2016)

As Multiprocessor System-on-Chips (MPSoCs) continue to scale, security for Network-on-Chips (NoCs) is a growing concern as rogue agents threaten to infringe on the hardware's trust and maliciously implant Hardware Trojans (HTs) to undermine their reliability. The trustworthiness of MPSoCs will rely on our ability to detect Denial-of-Service (DoS) threats posed by the HTs and mitigate HTs in a compromised NoC to permit graceful network degradation. In this paper, we propose a new light-weight target-activated sequential payload (TASP) HT model that performs packet inspection and injects faults to create a new type of DoS attack. Faults injected are used to trigger a response from error correction code (ECC) schemes and cause repeated retransmission to starve network resources and create deadlocks capable of rendering single-application to full chip failures. To circumvent the threat of HTs, we propose a heuristic threat detection model to classify faults and discover HTs within compromised links. To prevent further disruption, we propose several switch-to-switch link obfuscation methods to avoid triggering of HTs in an effort to continue using links instead of rerouting packets with minimal overhead (1-3 cycles). Our proposed modifications complement existing fault detection and obfuscation methods and only adds 2% in area overhead and 6% in excess power consumption in the NoC micro-architecture.
Quadrifilar helical antennas : Wideband and multiband behavior for GPS applications
(2010)

A novel multi-frequency printed quadrifilar helix antenna based on Multiple arm technique is presented in this paper. Double frequency and satisfactory antenna characteristics are achieved. The antenna has relatively compact size and hemispherical pattern with excellent circularly polarized coverage. The antenna is designed and simulated with the application of HFSS software. The simulation results and analyses are presented.
A Broadband Passive Monopulse Comparator MMIC
(2017)

A broadband monopulse comparator MMIC (Monolithic Microwave Integrated Circuit) based on GaAs process is presented in this Letter. The comparator network constructed by three magic tees and one lumped power divider is proposed for one sum channel and two delta channels. The measurement results show that very wide frequency band from 15 to 30 GHz (66.7% relatively frequency bandwidth) with less than 2.5-dB loss can be achieved for the sum channel. And the null depth is more than 22 dB in 15–27GHz and 17 dB in 27–30 GHz for two delta channels. The total chip size is 3.4 mm 3.4 mm (<inline-formula> <tex-math notation="LaTeX">$0.26\lambda _{0}~0.26\lambda _{0}$ </tex-math></inline-formula> at the center frequency of 22.5 GHz).
Why did my car just do that ? Explaining semi-autonomous driving actions to improve driver understanding , trust , and performance
(2014)

This study explores, in the context of semiautonomous driving, how the content of the verbalized message accompanying the car’s autonomous action affects the driver’s attitude and safety performance. Using a driving simulator with an auto-braking function, we tested different messages that provided advance explanation of the car’s imminent autonomous action. Messages providing only “how” information describing actions (e.g., “The car is braking”) led to poor driving performance, whereas “why” information describing reasoning for actions (e.g., “Obstacle ahead”) was preferred by drivers and led to better driving performance. Providing both “how and why” resulted in the safest driving performance but increased negative feelings in drivers. These results suggest that, to increase overall safety, car makers need to attend not only to the design of autonomous actions but also to the right way to explain these actions to the drivers.
Public opinion on automated driving : Results of an international questionnaire among 5000 respondents
(2016)

This study investigated user acceptance, concerns, and willingness to buy partially, highly, and fully automated vehicles. By means of a 63-question Internet-based survey, we collected 5000 responses from 109 countries (40 countries with at least 25 respondents). We determined cross-national differences, and assessed correlations with personal variables, such as age, gender, and personality traits as measured with a short version of the Big Five Inventory. Results showed that respondents, on average, found manual driving the most enjoyable mode of driving. Responses were diverse: 22% of the respondents did not want to pay more than $0 for a fully automated driving system, whereas 5% indicated they would be willing to pay more than $30,000, and 33% indicated that fully automated driving would be highly enjoyable. 69% of respondents estimated that fully automated driving will reach a 50% market share between now and 2050. Respondents were found to be most concerned about software hacking/misuse, and were also concerned about legal issues and safety. Respondents scoring higher on neuroticism were slightly less comfortable about data transmitting, whereas respondents scoring higher on agreeableness were slightly more comfortable with this. Respondents from more developed countries (in terms of lower accident statistics, higher education, and higher income) were less comfortable with their vehicle transmitting data, with cross-national correlations between q = 0.80 and q = 0.90. The present results indicate the major areas of promise and concern among the international public, and could be useful for vehicle developers and other stakeholders. 2015 Elsevier Ltd. All rights reserved.
Effects of adaptive cruise control and highly automated driving on workload and situation awareness : A review of the empirical evidence
(2014)

Department of BioMechanical Engineering, Faculty of Mechanical, Maritime and Materials Engineering, Delft University of Technology, Mekelweg 2, 2628 CD Delft, The Netherlands Centre for Transport Studies, University of Twente, Drienerlolaan 5, 7522 NB Enschede, The Netherlands c TNO Human Factors, Kampweg 5, 3769 DE Soesterberg, The Netherlands d Transportation Research Group, Civil, Maritime, Environmental Engineering and Science, Engineering and the Environment, University of Southampton, United Kingdom
The expansion of Google Scholar versus Web of Science: a longitudinal study
(2013)

Web of Science (WoS) and Google Scholar (GS) are prominent citation services with distinct indexing mechanisms. Comprehensive knowledge about the growth patterns of these two citation services is lacking. We analyzed the development of citation counts in WoS and GS for two classic articles and 56 articles from diverse research fields, making a distinction between retroactive growth (i.e., the relative difference between citation counts up to mid-2005 measured in mid-2005 and citation counts up to mid-2005 measured in April 2013) and actual growth (i.e., the relative difference between citation counts up to mid-2005 measured in April 2013 and citation counts up to April 2013 measured in April 2013). One of the classic articles was used for a citation-by-citation analysis. Results showed that GS has substantially grown in a retroactive manner (median of 170 % across articles), especially for articles that initially had low citations counts in GS as compared to WoS. Retroactive growth of WoS was small, with a median of 2 % across articles. Actual growth percentages were moderately higher for GS than for WoS (medians of 54 vs. 41 %). The citation-by-citation analysis showed that the percentage of citations being unique in WoS was lower for more recent citations (6.8 % for citations from 1995 and later vs. 41 % for citations from before 1995), whereas the opposite was noted for GS (57 vs. 33 %). It is concluded that, since its inception, GS has shown substantial expansion, and that the majority of recent works indexed in WoS are now also retrievable via GS. A discussion is provided on quantity versus quality of citations, threats for WoS, weaknesses of GS, and implications for literature research and research evaluation.
Toward a Theory of Situation Awareness in Dynamic Systems
(1995)

Situational awareness has become an increasingly salient factor contributing to flight safety and operational performance, and the research has burgeoned to cope with the human performance challenges associated with the installation of advanced avionics systems in modern aircraft. The systematic study and application of situational awareness has also extended beyond the cockpit to include air traffic controllers and personnel operating within other complex, high consequence work domains. This volume offers a collection of essays that have made important contributions to situational awareness research and practice. To this end, it provides unique access to key readings that address the conceptual development of situational awareness, methods for its assessment, and applications to enhance situational awareness through training and design.
The Influence of Culture on Memory
(2011)

The study of cognition across cultures offers a useful approach to both identifying bottlenecks in information processing and suggesting culturespecific strategies to alleviate these limitations. The recent emphasis on applying cognitive neuroscience methods to the study of culture further aids in specifying which processes differ cross-culturally. By localizing cultural differences to distinct neural regions, the comparison of cultural groups helps to identify candidate information processing mechanisms that can be made more efficient with augmented cognition and highlights the unique solutions that will be required for different groups of information processors.
Multidisciplinary Perspectives on Music Emotion Recognition : Implications for Content and Context-Based Models
(2012)

The prominent status of music in human culture and every day life is due in large part to its striking ability to elicit emotions, which may manifest from slight variation in mood to changes in our physical condition and actions. In this paper, we first review state of the art studies on music and emotions from different disciplines including psychology, musicology and music information retrieval. Based on these studies, we then propose new insights to enhance automated music emotion recog-